1. Field of the Invention
The present invention relates to technology for supporting the analysis of data using a programmed computer.
2. Description of the Related Art
Recently, there has been a boom in the more strategic use of large volumes of data stored in a data warehouse. As a result, the OLAP (Online Analytical Procedure) has gained recognition as a tool for end-users. With the current OLAP the basic structure involves cross tabulation in which a user himself/herself finds a problem heuristically while organizing data in tabular form, and then ascertains the cause of the problem. More specifically, the OLAP freely uses various distinctly defined sections to summarize by summing up for every section, and then organize the data.
Here, drilling, slicing, dicing and the like are used as means for freely using sections. When the data is organized or analyzed by drilling, slicing, dicing and the like, after recognizing a problem, the analyst makes out a value of a cell in the cross tabulation, and discerns the cause of the problem. The analyst then repeats a process to reorganize the data in sections for verifying the cause of the problem.
The process of making out a value of a cell in the cross tabulation and discerning the cause of the problem, however, is affected largely by the experience and skill of the analyst. In addition, the method of capturing the data and of defining sections differs for every analyst. Furthermore, when an analyst carries out an idea, new data must be prepared. Hence the data analysis becomes more complicated.
As a means to solve these problems, the applicant of this application has proposed a control method wherein a data model indicating sections is externally controlled as meta data, and a method of applying a concept of a “control point” which flexibly defines sections of information different for each person (Japanese Unexamined Patent Publication No. 8-180072).
However, there are still problems which cannot be solved by these methods.
Namely, when summarization is performed by summing up, there is a tendency to conceal the problems, and hence problems per se cannot be found. Hence the advantage of the OLAP cannot be utilized. Furthermore, the mechanism to simplify the cross tabulation simply increases the number of sections, making it difficult to search a section in order to ascertain the cause of a problem. In addition, since recently, the market and business are always fluctuating, it is necessary to change the sections corresponding to the fluctuations. Therefore, a deeper insight is required for the data analysis, and methods with only a verifying approach using sections prepared in advance, cannot be made to correspond to the fluctuations.
Hence, data mining which attempts to solve these problems by means of a discovery approach has been developed. Data mining is a technique which effectively utilizes large volumes of data stored in a data warehouse, and which performs automatic extraction of useful data.
However, though data mining effectively utilizes the data stored in the data warehouse, it has the following problems.
Namely, the contents of processing of the data mining are in a black box, and hence users do not know what kind of processing is performed. In addition, in order to use data mining, high skills in mathematics and business are required. Thus end-users cannot use it easily. Furthermore, even if what looks like the cause of a problem can be searched, verification is very difficult.